
Our second dataset is derived from the MovieLens-1M recommender system benchmark [1]}.
This is a standard recommender system benchmark, where the goal is to predict the rating that users assign movies.
However, unlike previous work, in our experiments we treat the user features (age, gender, and occupation) as sensitive attributes (rather than as additional feature information for the recommendation task).
Following [2]} we treat this recommendation task as an edge prediction problem between users and movies, viewing the different possible ratings as different edge relations.
